The team's solution is “Learning Reverse ISP with Inaccurately Aligned Supervision", inspired by their previous work [1]}. In order to decrease the burden of the Reverse ISP Network, they take advantage of the traditional inverse tone mapping and gamma expansion [2]}, [3]}.
The pipeline of the method is shown in Figure REF . Note that the team adapts the solution for each track. At Track 1, since the misalignment is small, they use a 9M parameters model (LiteISP \(8.88\) M, GCM \(0.04\) M). Considering the spatial misalignment of inputs (RGBs) and targets (RAWs), they adopt a pre-trained PWC-Net [4]} to warp the target RAW images aligned with RGB inputs. GCM [1]} is also applied to diminish the effect of color inconsistency in the image alignment. However, since GCM alone is not enough to handle the issue satisfactorily, they use the color histogram to match the GCM output with sRGB images for better color mapping. Based on this framework, they also adopt Spectral Normalization Layer (SN) to improve model's stability and Test-time Local Converter [6]} to alleviate the inconsistency of image sizes between training and testing. By doing this, they can effectively improve performance.
At Track 2, due to the lens shading effect and strong misalignment, they use larger patches of 1536px randomly sampled from the FR image to train the model. Moreover they replace LiteISPNet [1]} with NAFNet [8]} as RevISPNet (see Figure REF ), this increases model's complexity to 116M parameters.
The method was implemented in PyTorch and trained using 2\(\times \)  V100 GPU for 5 days using cropped images, AdamW and batch size 6. Flip and rotation \(\times \) 8 data augmentation is used during training. The inference is done using FR images and self-ensembles to improve color inconsistency. The authors emphasize that general FR inference is better than patch-based.
